Accurate prediction of the rolling force is critical to assuring the quality of the final product in steel manufacturing. Exit thickness of plate for each pass is calculated from roll gap, mill spring, and predicted roll force. Ideal pass scheduling is dependent on a precise prediction of the roll force in each pass. This paper will introduce a concept that allows obtaining the material model parameters directly from the rolling process on an industrial scale by the uniform differential neural network. On the basis of the characteristics that the uniform distribution can fully characterize the solution space and enhance the diversity of the population, uniformity research on differential evolution operator is made to get improved crossover with uniform distribution. When its original function is transferred with a transfer function, the uniform differential evolution algorithms can quickly solve complex optimization problems. Neural network structure and weights threshold are optimized by uniform differential evolution algorithm, and a uniform differential neural network is formed to improve rolling force prediction accuracy in process control system.
The steel plates are used for applications such as shipbuilding, bridge construction, civil engineering, industrial machinery, and offshore structures, which require high quality and high strength for reliability. Recently, market demands are growing increasingly strict for highquality products in hot rolling. Particularly, the requirement for thickness precision in a rolling mill process is stricter than any other request. Good thickness precision is highly related with good rolling force prediction [
Plate mill scheme.
A platemaking process goes through the following steps. Initially, a slab is reheated to recrystallization temperature (about 1215–1230°C) in the furnace, and it is rolled to a final target plate after about a dozen passes in the plate mill. Then, the microstructure of plate is controlled by the phase transformation of austenite during the cooling processes.
After the slab’s extraction from the furnace, the operation sequences in the rolling section are determined by a pass calculation algorithm, which calculates the sequences of rolling operations required and predicts the characteristics of the plate after each pass. Figure
Process control system of a plate mill.
The plate rolling process is a complicated process with multiple variables, nonlinearity, and strong coupling. Because of the complexity of rolling environment, such as the changes of material constant, friction coefficient, surface roughness of roller, roll wear, oil film thickness, and lubrication condition, the set calculation results of the rolling force, rolling torque, front slide, and deformation resistance are different from the actual rolling process. The rolling force is the most important equipment parameter and technological parameter of rolling mill, for it is the important basis of plastic processing technology, equipment optimization design, and process control. The calculation accuracy of the rolling force directly affects the setting accuracy of the rolling schedule; besides, it is the key to make full use of the regulatory capacity of the thickness and shape control system and the key to improve the hit rate of the steel head. As the conventional rolling force is calculated by the rolling force mathematical model based on experience and statistics, there are some defects in the process of using. Firstly, for the purposes of online control, the general mathematical model is simplified under certain assumptions, so it cannot provide sufficiently accurate predictive value. Secondly, because of the variation of the measurement errors and system characteristics, the parameter errors of model are also great. Therefore, in order to improve the accuracy of rolling force setting, adaptive and selflearning methods based on instant information are used to modify the model [
Rolling schedule plays an important role in the process of plate rolling production. And an excellent rolling schedule is the basic guarantee for the production capacity of rolling mill, for it can improve the quality of products. The medium and thick plate rolling schedule mainly includes the reduction (load) system, the speed system, the temperature system, and the roller type system. Based on the technical requirements of steel, raw material conditions, temperature conditions, and the actual situation of production equipment, rolling schedule design can make artificial calculation or computer calculation to determine the actual reduction, noload roll gap, rolling speed, and other parameters with the use of mathematical formulas or charts; in the meanwhile, according to the adaptive correction and processing under condition of actual rolling, rolling schedule design can give full play to the equipment potential, increase production, guarantee quality, make operation easy, and make equipment safe.
To develop the correct rolling schedule, a reasonable reduction (load) distribution must be determined. Because of the characteristics of the plate rolling, the research on load distribution started very late, but whether it is the traditional optimization method or intelligent optimization method, the whole process of optimization is generally summarized as 4 steps: (1) Determine the objective function of rolling load distribution according to the actual production conditions; (2) determine the constraint conditions according to the actual production conditions; (3) choose the appropriate optimization method; (4) derive the extreme value of the objective function and obtain the process parameters when the objective function reaches its extreme value.
The rolling force model is in the core position in the plate model system, as it is an important parameter to develop technological system, adjust the mill, improve the product quality, expand the product range, fully and reasonably tap equipment potential, and check equipment strength. At present, Sims’ model is recognized as the most suitable model for hot rolling [
Rolling torque can be determined by rolling force or energy consumption:
The rolling power is related to the rolling torque and the roll speed:
Temperature is one of the most influential factors to the deformation resistance. With the increase of temperature, the strength indexes, including yield limit, strength limit, and hardness, of all kinds of metals and alloys will decrease. This is because, with the increase of temperature, the amplitude of the thermal agitation of the metal atom increases and the bond force between the atoms decreases; accordingly the needed energy and the deformation resistance of the metal plastic deformation decrease. Generally plate temperature model includes the temperature drop of radiation, descaling, and interstand cooling water when the rolling plate is conveyed on the roll table or in the mill housing, the temperature rise of rolling deformation, and the temperature drop of contact between the rolling plate and the roll table or roller [
DE (differential evolution) algorithm proposed by Storn and Price in 1995 is a random group evolutionary search algorithm, which can guide the optimization process by the swarm intelligence produced by individual cooperation and competition. Due to the ease of use, robustness, and strong global optimization ability, DE algorithm has been successfully used in many fields [
When DE algorithm is solving optimization problems, the parallel search will be completed by
Set
In order to establish the initial point of optimal search, the population should be initialized first. One method of generating the initial population is randomly chosen from the given boundary constraints. In the DE research, it is generally assumed that all the randomly initialized populations accord with uniform probability distribution.
Set parameter variable boundary as
For each target individual
In order to increase the diversity of the population, the crossover operation is introduced. Variant individual
Formula (
In order to ensure that the better individual is selected into the next generation, DE compares the trial vector
In the problem of boundary constraints, it is necessary to make sure that the new individual is located in the feasible region of the problem, and a simple method is replaced by the random generation of the new individual with the feasible region.
That is, if
In the practical application, the differential evolution algorithm is developed for the convenience of representation, which is described in the form of DE/x/y/z.
The letter x represents the selection method of the base vector (the mutant target individual) of the mutation operation, and the letter x can be “
The letter y represents the number of differential vectors used.
The letter z represents the crossover method and is usually performed by Bernoulli experiment method with “
In accordance with the above provisions, the aforementioned differential evolution algorithm can be expressed as
Mode 1:
Mode 2:
Mode 3:
Mode 4:
Different deformation modes have their own characteristics, but through a large number of experimental studies, Storn and Price show that the performance of
In view of the disadvantages such as slow convergence speed and the decrease of population diversity, the algorithm is improved by using the information and target information to improve the spatial characteristics and the complex optimization environment. The uniform distribution proposed by [
In the DE algorithm, the variable to be optimized can be directly processed as the algorithm individual. Let
According to the first two sections, the specific steps of the AtDE algorithm are as follows.
Set
Calculate the fitness of each individual in the population.
Evaluate whether the optimal individual is located in the local minimum. If yes, then let
If the shutdown conditions are met, then shut the algorithm down; else go to the next step.
Perform mutation operation
Use uniform crossover operator for the original population and the intermediate population, and get the new intermediate population
Get the new population of the next generation by operator selection.
Set
Because the mathematical model has a solid theoretical basis and it can roughly predict the changing trend of the rolling force, the prediction model of the combination of the mathematical model and the uniform differential neural network is used in the setting calculation. The prediction model is shown as follows:
The advantages of the algorithm are as follows: on the one hand, because of the introduction of a neural network model with strong nonlinear approximation ability, the relationship between the parameters and the rolling force is well described, moreover, by online learning and realtime feedback correction, the model further improves the adaptability of the rolling force online model to the parameters variation and random disturbance, and then the prediction accuracy of the model is improved; on the other hand, because of the existence of the conventional model, the adaptive learning speed is improved, and the increment of the control of the intermediate variable is guaranteed not to be too large, so the stability of the rolling force prediction is improved, and the prediction accuracy of the rolling force is further improved. The application strategy of uniform differential neural network rolling force prediction model is shown in Figure
Application strategy of uniform differential neural network rolling force prediction model.
The input and output layer node number of neural network is designed according to the requirement of the user. On the basis of the information required to ensure the accuracy of network prediction model, the system size should be reduced as much as possible so as to reduce the learning time and the complexity of the system. The basic form of rolling force mathematical model can be expressed as
Considering that the working roll radius is relatively stable in the process of steady state rolling, in order to reduce the complexity of the network, the parameter
Therefore, the following nine main factors affecting the rolling force are set as the input parameters of the network: the entrance thickness, the reduction rate, the roll gap, the entrance width, the rolling speed, the rolling temperature, and contents of
The goal of improving the accuracy of the network can be achieved by increasing the number of hidden layers as well as the number of hidden layer nodes. The latter is simpler in implementation, so in this paper the hidden layer is fixed to one layer, and only the node number of the single hidden layer is automatically optimized. The node activation functions of both the hidden layer and output layer are Sigmoid functions.
The error data and the noise data will be removed before training. At the same time, the different variables represent different physical quantities, and their range may vary greatly, so all the data must be normalized to the same range of values. Because the activation function is the Sigmoid function, in order to avoid working in the flat area of the function, the data need to be transformed to 0.1–0.9. For this purpose, the following transformation is done on the data of the training sample set and the prediction sample set [
After the weights are obtained by training uniform differential neural network, the output value of the output layer is obtained by using the prediction sample, and the rolling force deviation value must be obtained by the transformation:
By using the uniform differential evolution algorithm, the code string for optimizing the structure and weights of the neural network consists of five parts: the number of the hidden nodes, the connection weights between the input layer and the hidden layer, the connection weights between the hidden layer and the output layer, the hidden layer threshold, and the output layer threshold. Set the numbers of input units, hidden units, and output units of the single hidden layer network to be
Because the number of the hidden nodes is indefinite, the length of code string is variable during network optimization, and inconvenience will be brought to the operator’s operation. At first, the maximum possible length of the code string is chosen to determine the maximum possible value
The total length of code string
The total length of the code string
Let the sample pair of training set be the input and the expected output of the uniform differential neural network, calculate the error between the network output and the expected output, and take the error sum of squares as the fitness function
The other parameter settings of the uniform DE algorithm are as follows. The population size is 60, the maximum evolution generation is 2000,
The schematics of uniform DE algorithm optimizing neural network are shown in Figure
Schematics of uniform DE algorithm optimizing neural network.
After the data collection of a plate mill for a period of time, 300 sets of data with good shape are selected to form a set of training samples. The structural parameters and the weight thresholds of the neural network are optimized by the uniform DE algorithm. The number of the hidden layer neurons is 11, and Table
Optimized inputtohidden layer weights.

 

1  2  3  4  5  6  7  8  9  
1  0.4456  −0.2414  0.1351  −0.0842  −0.4818  0.1771  0.9440  −0.0075  −0.4579 
2  0.0448  0.1204  −0.0670  −0.7016  0.7175  0.1968  −0.4644  0.7049  0.9254 
3  −0.3353  0.1644  −0.7372  0.2863  0.0220  0.5553  −0.3442  −0.1290  0.5077 
4  −0.2948  0.1012  −0.2111  −0.5865  0.0200  0.4889  0.2332  −0.5547  0.6458 
5  0.4114  −0.4637  0.1488  0.2867  0.0850  0.0605  0.1048  −0.8742  0.8624 
6  0.8308  0.8013  −0.7589  0.2929  0.6459  −0.6507  0.0033  −0.0798  −0.6997 
7  0.4553  −0.5299  −0.3334  0.6369  −0.3279  0.1081  0.1722  0.7468  0.3356 
8  0.7016  −0.1118  0.8840  −0.1757  −0.3861  0.2471  −0.0591  0.4828  −0.3004 
9  0.4182  0.2965  −0.3703  −0.3109  −0.5969  0.0480  0.6277  0.3526  −0.5174 
10  0.3091  0.6404  −0.7189  0.2736  −0.0714  0.1908  0.0999  −0.1387  0.8122 
11  0.0782  −0.1496  0.3921  0.2717  −0.8132  −0.0330  −0.3873  −0.9612  0.2904 
At the same time, the weights of the hidden layer and the output layer are 0.3193, −0.7575, −0.3996, −0.4072, −0.6635, 0.0252, 0.0334, 0.8492, 0.4070, −0.4100, and −0.5028, the hidden layer thresholds are −0.0879, −0.3755, −0.0685, 0.7466, −0.1059, 0.1128, 0.4648, 0.4294, 0.8185, −0.5616, and 0.4474, and the output layer threshold is 0.1540.
The neural network optimized by uniform differential algorithm can effectively avoid falling into local minima and speed up the training speed of the network. As shown in Figure
Convergence speed comparison between BP neural network and AtDE neural network.
According to the network structure of the above, the 80 sets of data are selected as the trial sample. As shown in Figure
Comparison between AtDE neural network model and the conventional model of rolling force prediction value and the measured value.
In this paper, a uniform differential evolution algorithm with good performance in high dimensional function optimization is used to optimize the structure and weight value of neural network, so as to constitute a uniform differential neural network, and then it is used to improve the accuracy of rolling force prediction in the process control system of medium and heavy plate. Specific research contents are as follows:
The basic theory and its algorithm improvement measures of BP neural network are analyzed.
In view of some defects of BP neural network, the AtDE algorithm is constructed, and the structure parameters and the weights of the neural network are optimized by using uniform DE algorithm, accordingly the performance of the neural network is improved, and the algorithm foundation of the adaptive learning of plate rolling force is laid.
On the basis of analysis of the traditional rolling force prediction model, the method of applying neural network to the rolling force prediction is discussed, and the rolling force prediction model based on uniform differential neural network is established. The results show that the model can improve the prediction accuracy.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This work is partially supported by National Natural Science Funds of China (51404021), Beijing Municipal Natural Science Foundation (3154035), and the Fundamental Research Funds for the Central Universities (FRFTP15061A3).